Cancer-related fatigue (CRF) is the most common symptom associated with cancer and its treatments. Moderate to severe CRF has a negative impact on patients? ability to tolerate treatments as well as on their quality of life. In some patients, CRF is so severe, that they discontinue cancer treatment. Given its high occurrence and significant negative impact, it is imperative that effective treatments be developed for this devastating symptom. Two of the major knowledge gaps for CRF are a lack of a risk prediction model and a lack of knowledge of its underlying mechanisms. A sensitive and specific risk prediction model would assist clinicians to determine which patients are most likely to experience high levels of CRF and provide recommendations regarding activity modifying interventions (e.g., exercise). Increased knowledge of the mechanisms for CRF could identify potential targets for therapeutic interventions. Both of these knowledge gaps will be addressed in this application. This study will use multiple sources of ?omics? data to investigate the molecular mechanisms associated with the severity of CRF in a well characterized sample of oncology patients (n=1343) who are experiencing low versus high levels of morning and evening CRF. Because these patients are undergoing chemotherapy (CTX), our study will investigate CTX-related fatigue (CTXRF). We will use a multi-staged analysis to integrate the gene expression, genetic, and epigenetic data. We will take advantage of the functional candidate genes identified in a gene expression profiling analyses to provide loci for analysis in subsequent genetic and epigenetic analyses. Candidate genes and pathways identified in this study will provide new and needed information on CTXRF mechanisms, as well as potential therapeutic targets. Prior studies suggest that patients will experience an increase in the severity of CTXRF in the week following CTX. However, no models exist to predict the magnitude of this increase. This inability to predict the severity of CTXRF during subsequent cycles of CTX limits the ability of clinicians to identify high-risk patients and provide them with recommendations to manage CTXRF. To address this knowledge gap, we propose to use demographic, clinical, and omics data to develop a model to predict the severity of morning and evening CTXRF experienced by a patient one week following CTX based on their profile for CTXRF in the week prior to the receipt of this cycle of CTX. This study will provide new insights to be able to identify high-risk patients as well as identify potential therapeutic targets. This project will guide the development and clinical studies to investigate additional mechanisms and therapeutic interventions for CTXRF and other types of fatigue associated with cancer and its treatment (e.g., radiation therapy, surgery).